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RISS 인기검색어
- 검색키워드
- 저자 : Hari Shanker Srivastava (검색결과 2 건)
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Selvaraj Shanmugapriya,Haldar Dipanwita,Srivastava Hari Shanker 대한공간정보학회 2021 Spatial Information Research Vol.29 No.5
The study was focused on assessing the condition of pearl millet crop in critical growth stages using both polarimetric Radarsat-2 and dual-polarized Sentinel-1 datasets. The results revealed that bajra having a close structured phenology like maize and Jowar, exhibited significant changes in RVI due to differences in the crop calendar dates. For bajra, polarimetric RVI generated from information rich Radarsat-2 was observed to have a higher level of saturation till 6 kgm-2 biomass with a R2 of 0.7. In all instances, RVI exhibited a significant relationship with VWC and plant volume with a R2 above 0.7 due to its higher sensitivity towards crop dielectric constant. Unlike NDVI, RVI increased with an increase in Leaf Area Index till 5.8 even during panicle initiation stage. Backscatter and truncated RVI almost follow a similar trend of RVI response for various crop growth parameters. Hence, in case of regional analysis and high cost of Radarsat-2 dataset, one can use freely available sentinel data for RVI analysis due to its wider swath coverage. The observed high correlation of crop age with RVI (R2 = 0.6) proved to be the best tool for predicting sowing dates in staggered sowing zones.
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Using multi-source data and decision tree classification in mapping vegetation diversity
Gaurav Shukla,Rahul Dev Garg,Pradeep Kumar,Hari Shanker Srivastava,Pradeep Kumar Garg 대한공간정보학회 2018 Spatial Information Research Vol.26 No.5
This study acknowledges the problem of land cover demarcation in diverse vegetation condition. The Normalized Difference Vegetation Index is used for the preparation of base map. Further identification of mix and incorrect classes was done using ground truth. Radar data in combination with optical indices are used. In different NDVI classes, rRV with additional criteria on Normalized Difference Water Index successfully demarcated waterlogged area, polarization ratio rRV/rRH and backscattering coefficient rRH are found suitable to separate bare land from dry grass land, sparse and dense scrub could be separated by - (rRV ? rRH)/2 and NDVI is efficient to identify dense vegetation. The study area is taken as Keoladeo National Park in Bharatpur, India. Statistical similarity between ground truth and classified class has been assessed using Jaccard coefficient (JC), Jaccard distance (JD), Dice coefficient (DC) and F-score. High similarity values of JC, JD, DC and F-score are achieved for all land cover types except bare land. Although, dry grassland showed low value of F-score; the reason could be low precision of class. The overall accuracy (87.17%), producer’s accuracy (86.39%), user’s accuracy (85.81%) and Kappa Coefficient (0.84) are also utilized to analyze performance of classifier.
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